Cole David M, Diaconescu Andreea O, Pfeiffer Ulrich J, Brodersen Kay H, Mathys Christoph D, Julkowski Dominika, Ruhrmann Stephan, Schilbach Leonhard, Tittgemeyer Marc, Vogeley Kai, Stephan Klaas E
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Psychiatric Hospital of the University of Zurich, Zurich, Switzerland.
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich, Switzerland; Department of Psychiatry (UPK), University of Basel, Basel, Switzerland; Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada.
Neuroimage Clin. 2020;26:102239. doi: 10.1016/j.nicl.2020.102239. Epub 2020 Mar 7.
Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour - with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental 'volatility' - and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals' behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.
当前的精神病理论强调异常学习信号，即预测误差（PEs）和不确定性在妄想信念形成中的作用。我们采用行为计算分析和功能磁共振成像（fMRI）来检查这种异常在临床高危（CHR）个体中是否明显。未用药的CHR个体（n = 13）和对照参与者（n = 13）在fMRI数据采集期间执行了概率学习范式。我们使用分层贝叶斯模型从行为中推断个体特定的计算——重点关注不同层面的预测误差和不确定性（或其倒数，精度），包括环境“波动性”——并将这些计算量用于fMRI数据分析。CHR个体行为的计算建模表明，波动性估计收敛到显著高于对照组的水平。基于模型的fMRI显示，CHR个体前额叶和脑岛区域对精度加权的低水平结果预测误差的反应增强，而较高水平预测误差（用于更新波动性估计）对前额叶、眶额和前脑岛皮质的激活减少。此外，CHR个体对结果预测误差的前额叶皮质活动与整体功能的临床测量呈负相关。我们的结果表明，在环境不确定性条件下，CHR个体存在多方面的学习异常，包括更高水平的波动性估计、皮质激活减少，以及精度加权的结果预测误差导致前额叶和脑岛区域异常高激活。这种高低水平学习信号的非典型表现可能反映了妄想形成的易感性。
